In [1]:
cd ../..

/Users/shanekercheval/repos/data-science-template
In [2]:
%run "source/config/notebook_settings.py"

Load Data¶

In [3]:
file_name = 'artifacts/models/experiments/multi-model-BayesSearchCV-2022-03-19-14-44-37.yaml'
results = hlp.sklearn_eval.MLExperimentResults.from_yaml_file(yaml_file_name = file_name)

Hyper-Param Tuning - Cross Validation Results¶

Best Scores/Params¶

In [4]:
results.best_score
Out[4]:
0.774092779234226
In [5]:
results.best_params
Out[5]:
{'model': 'LogisticRegression()',
 'C': 0.13184996310179986,
 'imputer': "SimpleImputer(strategy='median')",
 'scaler': 'StandardScaler()',
 'pca': 'None',
 'encoder': 'OneHotEncoder()'}
In [6]:
# Best model from each model-type.
df = results.to_formatted_dataframe(return_style=False, include_rank=True)
df["model_rank"] = df.groupby("model")["roc_auc Mean"].rank(method="first", ascending=False)
df.query('model_rank == 1')
Out[6]:
rank roc_auc Mean roc_auc 95CI.LO roc_auc 95CI.HI model C max_features max_depth n_estimators min_samples_split min_samples_leaf max_samples criterion learning_rate min_child_weight subsample colsample_bytree colsample_bylevel reg_alpha reg_lambda imputer scaler pca encoder model_rank
5 1 0.77 0.73 0.82 LogisticRegression() 0.13 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN SimpleImputer(strategy='median') StandardScaler() None OneHotEncoder() 1.00
18 2 0.77 0.72 0.81 RandomForestClassifier() NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN SimpleImputer() None None OneHotEncoder() 1.00
7 4 0.76 0.72 0.80 LinearSVC() 0.28 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN SimpleImputer(strategy='most_frequent') MinMaxScaler() PCA('mle') OneHotEncoder() 1.00
16 6 0.76 0.70 0.82 ExtraTreesClassifier() NaN 0.68 30.00 1659.00 25.00 11.00 0.78 gini NaN NaN NaN NaN NaN NaN NaN SimpleImputer() None PCA('mle') OneHotEncoder() 1.00
28 9 0.75 0.71 0.80 XGBClassifier() NaN NaN 5.00 1246.00 NaN NaN NaN NaN 0.02 15.00 0.96 0.69 0.52 0.24 1.22 SimpleImputer(strategy='median') None None OneHotEncoder() 1.00
In [7]:
results.to_formatted_dataframe(return_style=True,
                               include_rank=True,
                               num_rows=1000)
Out[7]:
rank roc_auc Mean roc_auc 95CI.LO roc_auc 95CI.HI model C max_features max_depth n_estimators min_samples_split min_samples_leaf max_samples criterion learning_rate min_child_weight subsample colsample_bytree colsample_bylevel reg_alpha reg_lambda imputer scaler pca encoder
1 0.774 0.730 0.818 LogisticRegression() 0.132 <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> SimpleImputer(strategy='median') StandardScaler() None OneHotEncoder()
2 0.767 0.720 0.814 RandomForestClassifier() <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> SimpleImputer() None None OneHotEncoder()
3 0.763 0.725 0.802 LogisticRegression() <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> SimpleImputer() StandardScaler() None OneHotEncoder()
4 0.761 0.720 0.803 LinearSVC() 0.281 <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> SimpleImputer(strategy='most_frequent') MinMaxScaler() PCA('mle') OneHotEncoder()
5 0.761 0.697 0.825 LogisticRegression() 0.001 <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> SimpleImputer(strategy='median') MinMaxScaler() None OneHotEncoder()
6 0.760 0.701 0.819 ExtraTreesClassifier() <NA> 0.685 30.000 1,659.000 25.000 11.000 0.781 gini <NA> <NA> <NA> <NA> <NA> <NA> <NA> SimpleImputer() None PCA('mle') OneHotEncoder()
7 0.755 0.714 0.796 RandomForestClassifier() <NA> 0.599 70.000 1,858.000 39.000 22.000 0.851 gini <NA> <NA> <NA> <NA> <NA> <NA> <NA> SimpleImputer(strategy='most_frequent') None None OneHotEncoder()
8 0.753 0.716 0.791 RandomForestClassifier() <NA> 0.303 81.000 1,063.000 15.000 27.000 0.502 gini <NA> <NA> <NA> <NA> <NA> <NA> <NA> SimpleImputer(strategy='median') None None OneHotEncoder()
9 0.753 0.710 0.796 XGBClassifier() <NA> <NA> 5.000 1,246.000 <NA> <NA> <NA> <NA> 0.023 15.000 0.956 0.695 0.519 0.242 1.221 SimpleImputer(strategy='median') None None OneHotEncoder()
10 0.752 0.698 0.805 ExtraTreesClassifier() <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> SimpleImputer() None None OneHotEncoder()
11 0.751 0.721 0.781 LinearSVC() <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> SimpleImputer() StandardScaler() None OneHotEncoder()
12 0.751 0.714 0.788 RandomForestClassifier() <NA> 0.323 76.000 1,619.000 31.000 36.000 0.595 gini <NA> <NA> <NA> <NA> <NA> <NA> <NA> SimpleImputer() None None OneHotEncoder()
13 0.749 0.698 0.801 XGBClassifier() <NA> <NA> 1.000 1,974.000 <NA> <NA> <NA> <NA> 0.024 4.000 0.543 0.620 0.876 0.034 1.445 SimpleImputer() None PCA('mle') OneHotEncoder()
14 0.749 0.706 0.792 ExtraTreesClassifier() <NA> 0.408 87.000 1,423.000 25.000 19.000 0.989 entropy <NA> <NA> <NA> <NA> <NA> <NA> <NA> SimpleImputer(strategy='most_frequent') None PCA('mle') CustomOrdinalEncoder()
15 0.747 0.694 0.799 ExtraTreesClassifier() <NA> 0.710 15.000 1,493.000 33.000 27.000 0.914 gini <NA> <NA> <NA> <NA> <NA> <NA> <NA> SimpleImputer(strategy='most_frequent') None PCA('mle') OneHotEncoder()
16 0.746 0.708 0.785 XGBClassifier() <NA> <NA> 3.000 931.000 <NA> <NA> <NA> <NA> 0.029 7.000 0.930 0.817 0.898 0.000 2.733 SimpleImputer(strategy='most_frequent') None None OneHotEncoder()
17 0.746 0.716 0.776 LogisticRegression() 23.327 <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> SimpleImputer(strategy='median') StandardScaler() None OneHotEncoder()
18 0.744 0.698 0.790 ExtraTreesClassifier() <NA> 0.328 5.000 1,047.000 23.000 43.000 0.957 entropy <NA> <NA> <NA> <NA> <NA> <NA> <NA> SimpleImputer(strategy='median') None PCA('mle') OneHotEncoder()
19 0.744 0.709 0.779 RandomForestClassifier() <NA> 0.567 38.000 1,060.000 19.000 41.000 0.656 entropy <NA> <NA> <NA> <NA> <NA> <NA> <NA> SimpleImputer() None PCA('mle') OneHotEncoder()
20 0.742 0.686 0.798 XGBClassifier() <NA> <NA> 10.000 1,146.000 <NA> <NA> <NA> <NA> 0.025 14.000 0.771 0.548 0.748 0.093 1.892 SimpleImputer(strategy='median') None PCA('mle') OneHotEncoder()
21 0.738 0.686 0.790 XGBClassifier() <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> SimpleImputer() None None OneHotEncoder()
22 0.736 0.695 0.777 ExtraTreesClassifier() <NA> 0.740 14.000 1,645.000 5.000 43.000 0.741 entropy <NA> <NA> <NA> <NA> <NA> <NA> <NA> SimpleImputer(strategy='most_frequent') None PCA('mle') CustomOrdinalEncoder()
23 0.734 0.695 0.773 RandomForestClassifier() <NA> 0.770 70.000 1,570.000 16.000 39.000 0.910 entropy <NA> <NA> <NA> <NA> <NA> <NA> <NA> SimpleImputer(strategy='most_frequent') None None CustomOrdinalEncoder()
24 0.730 0.702 0.758 LogisticRegression() 0.000 <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> SimpleImputer(strategy='median') StandardScaler() None CustomOrdinalEncoder()
25 0.727 0.690 0.765 LinearSVC() 0.361 <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> SimpleImputer(strategy='median') MinMaxScaler() PCA('mle') CustomOrdinalEncoder()
26 0.727 0.689 0.765 LinearSVC() 0.746 <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> SimpleImputer(strategy='median') MinMaxScaler() None CustomOrdinalEncoder()
27 0.726 0.697 0.755 LogisticRegression() 0.000 <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> SimpleImputer(strategy='median') StandardScaler() PCA('mle') CustomOrdinalEncoder()
28 0.714 0.679 0.749 XGBClassifier() <NA> <NA> 4.000 1,181.000 <NA> <NA> <NA> <NA> 0.067 7.000 0.557 0.763 0.592 0.001 2.984 SimpleImputer(strategy='median') None PCA('mle') CustomOrdinalEncoder()
29 0.701 0.669 0.733 LinearSVC() 10.021 <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> SimpleImputer() MinMaxScaler() PCA('mle') CustomOrdinalEncoder()
30 0.681 0.646 0.717 LinearSVC() 0.000 <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> SimpleImputer() StandardScaler() None CustomOrdinalEncoder()
In [8]:
results.to_formatted_dataframe(query='model == "RandomForestClassifier()"', include_rank=True)
Out[8]:
rank roc_auc Mean roc_auc 95CI.LO roc_auc 95CI.HI max_features max_depth n_estimators min_samples_split min_samples_leaf max_samples criterion imputer pca encoder
1 0.767 0.720 0.814 <NA> <NA> <NA> <NA> <NA> <NA> <NA> SimpleImputer() None OneHotEncoder()
2 0.755 0.714 0.796 0.599 70.000 1,858.000 39.000 22.000 0.851 gini SimpleImputer(strategy='most_frequent') None OneHotEncoder()
3 0.753 0.716 0.791 0.303 81.000 1,063.000 15.000 27.000 0.502 gini SimpleImputer(strategy='median') None OneHotEncoder()
4 0.751 0.714 0.788 0.323 76.000 1,619.000 31.000 36.000 0.595 gini SimpleImputer() None OneHotEncoder()
5 0.744 0.709 0.779 0.567 38.000 1,060.000 19.000 41.000 0.656 entropy SimpleImputer() PCA('mle') OneHotEncoder()
6 0.734 0.695 0.773 0.770 70.000 1,570.000 16.000 39.000 0.910 entropy SimpleImputer(strategy='most_frequent') None CustomOrdinalEncoder()
In [9]:
results.to_formatted_dataframe(query='model == "LogisticRegression()"', include_rank=True)
Out[9]:
rank roc_auc Mean roc_auc 95CI.LO roc_auc 95CI.HI C imputer scaler pca encoder
1 0.774 0.730 0.818 0.132 SimpleImputer(strategy='median') StandardScaler() None OneHotEncoder()
2 0.763 0.725 0.802 <NA> SimpleImputer() StandardScaler() None OneHotEncoder()
3 0.761 0.697 0.825 0.001 SimpleImputer(strategy='median') MinMaxScaler() None OneHotEncoder()
4 0.746 0.716 0.776 23.327 SimpleImputer(strategy='median') StandardScaler() None OneHotEncoder()
5 0.730 0.702 0.758 0.000 SimpleImputer(strategy='median') StandardScaler() None CustomOrdinalEncoder()
6 0.726 0.697 0.755 0.000 SimpleImputer(strategy='median') StandardScaler() PCA('mle') CustomOrdinalEncoder()

BayesSearchCV Performance Over Time¶

In [10]:
results.plot_performance_across_trials(facet_by='model').show()
In [11]:
results.plot_performance_across_trials(query='model == "RandomForestClassifier()"').show()

Variable Performance Over Time¶

In [12]:
results.plot_parameter_values_across_trials(query='model == "RandomForestClassifier()"').show()

Scatter Matrix¶

In [13]:
# results.plot_scatter_matrix(query='model == "RandomForestClassifier()"',
#                             height=1000, width=1000).show()

Variable Performance - Numeric¶

In [14]:
results.plot_performance_numeric_params(query='model == "RandomForestClassifier()"',
                                        height=800)
In [15]:
results.plot_parallel_coordinates(query='model == "RandomForestClassifier()"').show()

Variable Performance - Non-Numeric¶

In [16]:
results.plot_performance_non_numeric_params(query='model == "RandomForestClassifier()"').show()
In [17]:
results.plot_score_vs_parameter(
    query='model == "RandomForestClassifier()"',
    parameter='max_features',
    size='max_depth',
    color='encoder',
)
In [18]:
# results.plot_parameter_vs_parameter(
#     query='model == "XGBClassifier()"',
#     parameter_x='colsample_bytree',
#     parameter_y='learning_rate',
#     size='max_depth'
# )
In [19]:
# results.plot_parameter_vs_parameter(
#     query='model == "XGBClassifier()"',
#     parameter_x='colsample_bytree',
#     parameter_y='learning_rate',
#     size='imputer'
# )

Best Model - Test Set Performance¶

In [20]:
file_name = 'artifacts/models/experiments/multi-model-BayesSearchCV-2022-03-19-14-44-37_best_estimator.pkl'
best_estimator = hlp.utility.read_pickle(file_name)
In [21]:
x_test = pd.read_pickle('artifacts/data/processed/x_test.pkl')
x_test.head()
Out[21]:
checking_status duration credit_history purpose credit_amount savings_status employment installment_commitment personal_status other_parties residence_since property_magnitude age other_payment_plans housing existing_credits job num_dependents own_telephone foreign_worker
521 <0 18.00 existing paid radio/tv 3190.00 <100 1<=X<4 2.00 female div/dep/mar none 2.00 real estate 24.00 none own 1.00 skilled 1.00 none yes
737 <0 18.00 existing paid new car 4380.00 100<=X<500 1<=X<4 3.00 male single none 4.00 car 35.00 none own 1.00 unskilled resident 2.00 yes yes
740 <0 24.00 all paid new car 2325.00 100<=X<500 4<=X<7 2.00 male single none 3.00 car 32.00 bank own 1.00 skilled 1.00 none yes
660 >=200 12.00 existing paid radio/tv 1297.00 <100 1<=X<4 3.00 male mar/wid none 4.00 real estate 23.00 none rent 1.00 skilled 1.00 none yes
411 no checking 33.00 critical/other existing credit used car 7253.00 <100 4<=X<7 3.00 male single none 2.00 car 35.00 none own 2.00 high qualif/self emp/mgmt 1.00 yes yes
In [22]:
y_test = hlp.utility.read_pickle('artifacts/data/processed/y_test.pkl')
y_test[0:10]
Out[22]:
array([1, 0, 0, 0, 0, 0, 0, 0, 0, 0])
In [23]:
test_predictions = best_estimator.predict_proba(x_test)[:, 1]
test_predictions[0:10]
Out[23]:
array([0.403314  , 0.49843118, 0.59686198, 0.32004122, 0.0857983 ,
       0.35470202, 0.08364243, 0.45742452, 0.09325265, 0.13118732])
In [24]:
evaluator = hlp.sklearn_eval.TwoClassEvaluator(
    actual_values=y_test,
    predicted_scores=test_predictions,
    score_threshold=0.37
)
In [25]:
evaluator.plot_actual_vs_predict_histogram()
In [26]:
evaluator.plot_confusion_matrix()
In [27]:
evaluator.all_metrics_df(return_style=True,
                         dummy_classifier_strategy=['prior', 'constant'],
                         round_by=3)
Out[27]:
  Score Dummy (prior) Dummy (constant) Explanation
AUC 0.800 0.500 0.500 Area under the ROC curve (true pos. rate vs false pos. rate); ranges from 0.5 (purely random classifier) to 1.0 (perfect classifier)
True Positive Rate 0.729 0.000 1.000 72.9% of positive instances were correctly identified.; i.e. 43 "Positive Class" labels were correctly identified out of 59 instances; a.k.a Sensitivity/Recall
True Negative Rate 0.787 1.000 0.000 78.7% of negative instances were correctly identified.; i.e. 111 "Negative Class" labels were correctly identified out of 141 instances
False Positive Rate 0.213 0.000 1.000 21.3% of negative instances were incorrectly identified as positive; i.e. 30 "Negative Class" labels were incorrectly identified as "Positive Class", out of 141 instances
False Negative Rate 0.271 1.000 0.000 27.1% of positive instances were incorrectly identified as negative; i.e. 16 "Positive Class" labels were incorrectly identified as "Negative Class", out of 59 instances
Positive Predictive Value 0.589 0.000 0.295 When the model claims an instance is positive, it is correct 58.9% of the time; i.e. out of the 73 times the model predicted "Positive Class", it was correct 43 times; a.k.a precision
Negative Predictive Value 0.874 0.705 0.000 When the model claims an instance is negative, it is correct 87.4% of the time; i.e. out of the 127 times the model predicted "Negative Class", it was correct 111 times
F1 Score 0.652 0.000 0.456 The F1 score can be interpreted as a weighted average of the precision and recall, where an F1 score reaches its best value at 1 and worst score at 0.
Precision/Recall AUC 0.647 0.295 0.295 Precision/Recall AUC is calculated with `average_precision` which summarizes a precision-recall curve as the weighted mean of precisions achieved at each threshold. See sci-kit learn documentation for caveats.
Accuracy 0.770 0.705 0.295 77.0% of instances were correctly identified
Error Rate 0.230 0.295 0.705 23.0% of instances were incorrectly identified
% Positive 0.295 0.295 0.295 29.5% of the data are positive; i.e. out of 200 total observations; 59 are labeled as "Positive Class"
Total Observations 200 200 200 There are 200 total observations; i.e. sample size
In [28]:
evaluator.plot_roc_auc_curve().show()

<Figure size 1000x618.034 with 0 Axes>
In [29]:
evaluator.plot_precision_recall_auc_curve().show()
In [30]:
evaluator.plot_threshold_curves(score_threshold_range=(0.1, 0.7)).show()
In [31]:
evaluator.plot_precision_recall_tradeoff(score_threshold_range=(0.1, 0.6)).show()
In [32]:
evaluator.calculate_lift_gain(return_style=True)
Out[32]:
  Gain Lift
Percentile    
5 0.12 2.37
10 0.24 2.37
15 0.37 2.49
20 0.49 2.46
25 0.53 2.10
30 0.61 2.03
35 0.71 2.03
40 0.75 1.86
45 0.75 1.66
50 0.80 1.59
55 0.83 1.51
60 0.83 1.38
65 0.86 1.33
70 0.93 1.33
75 0.97 1.29
80 0.97 1.21
85 0.98 1.16
90 0.98 1.09
95 1.00 1.05
100 1.00 1.00